Currently it is very costly in terms of computational resources to analyze a high-volume live data stream and visualize it at an interactive rate on end user devices. This is especially true in the era of big data, as many of the decisions being made rely on efficient analysis and visualization of complex data. Indeed, it is nontrivial to process and visualize data both in real-time and in large scale.
Conventional preload-store-plot approaches to visualizing high volumes of data suffer a great amount of data I/O lag and, at their best, these approaches merely provide near real-time performance. Furthermore, front-end approaches to visualizations can be easily overwhelmed when the amount of data points grows too large. For example, many of today's visualization libraries (e.g., D3.js) are easy to program and rich in expressiveness; however, they cannot scale to tens of thousands of elements (and beyond). Solutions such as Level-Of-Detail scaling have been around for some time, but this barely mitigates performance issues encountered when users have the ability to display multiple linked views.